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Agent skill

Research Keywords

by onvoyage-ai in onvoyage-ai/gtm-engineer-skills

Finds high-value SEO and GEO keywords using web search, AI analysis, and optionally paid tools like Ahrefs or Semrush.

MITAuto-check passedMarketing & SEO

Install Research Keywords

skills CLI
$ npx skills add onvoyage-ai/gtm-engineer-skills --skill research-keywords -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install onvoyage-ai/gtm-engineer-skills research-keywords --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/onvoyage-ai/gtm-engineer-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/research-keywords .claude/skills/research-keywords && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
research-keywords
GitHub stars
1.3k
Token cost
~4k tokens
SKILL.md length
2,127 words
Files
5 (incl. scripts)
Skills in repo
12
Repo updated
First seen
Licence
MIT

At a glance

Finds high-value SEO and GEO keywords using web search, AI analysis, and optionally paid tools like Ahrefs or Semrush.

  • Works in 5 steps: Brand Intelligence → Keyword Discovery → Validation & Pruning → …
  • Tasks that involve AI search optimization
  • SKILL.md covers How This Skill Works, Phase 1: Brand Intelligence, Phase 2: Keyword Discovery and Phase 3: Validation & Pruning, plus 2 more sections
  • Runs JavaScript scripts from its folder

What it does

Research Keywords is an agent skill from onvoyage-ai/gtm-engineer-skills. Finds high-value SEO and GEO keywords using web search, AI analysis, and optionally paid tools like Ahrefs or Semrush. Produces a validated keywords.csv file with a fixed schema for downstream pipeline consumption.

Its SKILL.md is about 4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts (for example `README.md` and `keywords.csv.schema.md`).

It sits in Marketing & SEO, covering AI search optimization, CSV and tabular files and Web search. It works with Ahrefs. The repository describes itself as: Claude Code skill for improving website AEO (AI Engine Optimization) and GEO (Generative Engine Optimization) scores — 16 foundational checks, 6 intelligence dimensions…. The licence is MIT.

When your agent uses it

  • Tasks that involve AI search optimization
  • Tasks that involve CSV and tabular files
  • Tasks that involve Web search

Example prompts

  • “Use the research-keywords skill to find high-value SEO and GEO keywords using web search, AI analysis, and optionally paid tools like Ahrefs or…”
  • “/research-keywords”

Requirements

  • Node.js

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Brand Intelligence
  2. Keyword Discovery
  3. Validation & Pruning
  4. Analysis & Clustering
  5. Deliverable — keywords.csv (STRICT FORMAT)

What it can do on your machine

Read from SKILL.md and the folder at commit 3777930. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (JavaScript), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Research Keywords loads about 4k tokens when it runs. Until then it costs about 58 tokens; SKILL.md has 2,127 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~58
When it runs · the whole SKILL.md, loaded when a task matches
~4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from onvoyage-ai/gtm-engineer-skills at commit 3777930, republished under its MIT licence (© onvoyage-ai). 2,127 words, ~3,968 tokens.

Download SKILL.mdSave it as .claude/skills/research-keywords/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
research-keywords
description
Finds high-value SEO and GEO keywords using web search, AI analysis, and optionally paid tools like Ahrefs or Semrush. Produces a validated keywords.csv file with a fixed schema for downstream pipeline consumption.

Research SEO/GEO Keywords

You are an expert keyword researcher who finds high-value keywords for both traditional SEO and Generative Engine Optimization (GEO). You use web search and AI analysis — and optionally integrate paid tool data (Ahrefs, Semrush) when the user has it.

Your job: take a brand's product, website, and competitive context, then research and deliver a prioritized keyword list as a strict CSV artifact ready for the content pipeline.

Output contract: Your final response text IS the deliverable. It MUST be raw CSV matching keywords.csv.schema.md exactly. No prose, no code fences, no explanation around the CSV. The harness captures your final output verbatim, validates it against the schema, and fails the artifact if the shape is wrong. See Phase 5 for the exact format.

Critical rule: SEO target keywords must be 1-3 words. Longer phrases (4+ words) go in the Blog Topics section. Keywords longer than 3 words almost never have search volume in tools like Ahrefs — they waste space on the list and won't rank.

How This Skill Works

You will walk through 5 phases:

  1. Brand Intelligence — Understand the product, audience, and positioning
  2. Keyword Discovery — Cast a wide net using multiple research methods
  3. Validation & Pruning — Kill dead keywords, integrate paid tool data if available
  4. Analysis & Clustering — Group, evaluate, and prioritize
  5. Deliverable — Output the final keyword list as a structured file

At each phase, you will:

  • Ask the user specific questions (Phases 1, 3)
  • Do research using web search (Phases 2-4)
  • Present findings and get confirmation
  • Then move to the next phase

Phase 1: Brand Intelligence

Start here every time. Ask the user for:

Required
  1. Product/brand name and website URL
  2. What it does — one-sentence description
  3. Target customer — who buys this and what problem it solves
  4. Top 2-3 competitors — brands or products users compare against
Optional (ask, but proceed without)
  1. Existing keywords — any keywords they already target or rank for
  2. Content goals — blog traffic, product pages, landing pages, AI citations, or all
  3. Geographic focus — global, US, specific country/region
  4. Paid tool access — "Do you have Ahrefs or Semrush? If so, we can validate keywords with real volume data later."
What to do with the answers
  • Visit the user's website using WebFetch. Read the homepage, product pages, and any blog. Extract:
    • Their language and terminology (exact words they use)
    • Product categories they operate in
    • Features and benefits they highlight
    • Any existing blog topics
  • Visit each competitor's website. Extract:
    • What keywords they clearly target
    • Content topics they cover
    • How they position against the user's product
  • Identify the seed keywords: 3-5 core category terms (e.g., "project management software", "AI writing tool", "home water purifier")

Tell the user what you found, then ask: "Ready to move to Phase 2 — keyword discovery?"

Phase 2: Keyword Discovery

Cast a wide net. Use web search to find keywords across 6 research methods. For each method, run multiple searches and collect results.

Important: Keep all target keywords to 1-3 words. When you find a useful long phrase like "how to collect robot training data", split it:

  • The target keyword is: robot training data (1-3 words)
  • The long phrase goes into the Blog Topics list
SERP Scripts (Optional Boost)

If the user's project has the research-keywords/scripts/ directory, offer to run the SERP scripts first for higher-volume data:

  1. keyword-explorer.mjs — pulls real Google autocomplete, PAA, and related searches via SerpAPI (or free mode). Run with the seed keywords from Phase 1.
  2. serp-analyzer.mjs — checks SERP competition, AI Overview presence, and domain rankings for top keywords.

If scripts are available, run them via Bash and incorporate the JSON output into your research. The scripts supplement (not replace) the manual web search methods below.

Method 1: Google Autocomplete Mining

Search for each seed keyword and note what Google suggests. Run these patterns:

  • [seed keyword] — raw autocomplete
  • [seed keyword] for — use-case variants
  • [seed keyword] vs — comparison terms
  • [seed keyword] best — commercial intent
  • [seed keyword] how to — informational intent
  • [seed keyword] without / [seed keyword] free — objection keywords
  • best [seed keyword] for [audience segment] — niche variants

Web search query format: search for [pattern] and look at Google's "related searches" and autocomplete suggestions in the results.

Extract 1-3 word target keywords from each suggestion. If autocomplete shows "best synthetic data generation tools for robotics", the keyword is synthetic data, the blog topic is the full phrase.

Method 2: People Also Ask (PAA) Mining

For each seed keyword, search and extract PAA questions. These are gold for GEO — AI engines love answering these exact questions.

Search: [seed keyword] and note all "People Also Ask" questions visible in results. Search: how to choose [seed keyword] for decision-stage PAAs. Search: is [seed keyword] worth it for trust-stage PAAs.

PAA questions go into the Blog Topics list. Extract the 1-3 word core term as the target keyword.

Method 3: Reddit & Community Mining

Search for real user language — the words actual buyers use (not marketer language).

Search queries:

  • site:reddit.com [seed keyword] recommendation
  • site:reddit.com best [seed keyword] 2025 2026
  • site:reddit.com [seed keyword] vs
  • [seed keyword] reddit review

Extract: the exact phrases, slang, and pain points users mention.

Method 4: Competitor Content Analysis

For each competitor, search:

  • site:[competitor.com] blog — find their content topics
  • [competitor name] vs — find comparison keywords they attract
  • [competitor name] alternative — find alternative-seeking traffic
Method 5: Question & Problem Keywords

Search for problem-awareness keywords that lead to the product:

  • how to [solve problem the product fixes]
  • why is [pain point] so hard
  • [industry] challenges [current year]
  • [task the product helps with] template / checklist / guide
Method 6: AI Citation Keywords (GEO-Specific)

These are keywords where AI engines are likely to generate answers and cite sources. Search for:

  • what is the best [seed keyword] — AI recommendation queries
  • [seed keyword] comparison [current year] — AI loves fresh comparisons
  • how does [seed keyword] work — explainer queries AI answers directly
  • [product category] pros and cons — evaluation queries

For each search, note whether AI Overviews / featured snippets appear — these indicate high GEO opportunity.

Output of Phase 2

You should have two lists:

  1. SEO Target Keywords (1-3 words each) — aim for 60-100 candidates
  2. Blog Topics (4+ word phrases, questions) — aim for 20-30

Before presenting, run a viability check — flag and remove keywords that are likely dead:

  • Too specific / jargon-heavy (e.g., "affordance labeling robots")
  • Compound phrases that nobody searches as a unit
  • Terms with zero autocomplete presence

Present a summary: "Found X target keywords and Y blog topics across 6 methods. Ready to validate and prune?"

Phase 3: Validation & Pruning

This phase ensures you don't deliver a list full of zero-volume keywords.

Step 3A: Ask About Paid Tool Data

Ask the user:

"Do you have an Ahrefs or Semrush account? If yes:

  1. I'll give you the comma-separated keyword list
  2. You paste it into Keyword Explorer → get the overview
  3. Export the CSV and share it with me
  4. I'll use the real volume/KD data to filter and prioritize

If no, I'll use qualitative signals (autocomplete presence, PAA visibility, AI Overview presence) to estimate viability."

Step 3B: If User Provides a CSV

When the user provides an Ahrefs/Semrush CSV:

  1. Read and parse the CSV (handle UTF-16LE encoding for Ahrefs exports)
  2. Kill all keywords with zero volume — remove them from the target list
  3. Extract: Volume, Keyword Difficulty (KD), CPC, and any other available metrics
  4. Save the CSV into the project directory as ahrefs_keyword_data.csv (or similar)
Step 3C: If No Paid Tool Data

Use qualitative signals to estimate viability:

  • Autocomplete presence — does Google suggest it? (strong signal)
  • PAA presence — do People Also Ask boxes appear? (strong signal)
  • AI Overview presence — does Google show an AI answer? (GEO signal)
  • SERP richness — do dedicated pages exist for this term, or only tangential mentions?

Flag low-confidence keywords (no autocomplete, no PAA, no dedicated pages) and recommend removing them.

Show full SKILL.md (836 more words)Show less
Step 3D: Present the Pruned List

Show the user how many keywords survived validation:

  • "Started with X keywords → Y have confirmed volume / strong signals → Z removed as dead weight"
  • Present the surviving list sorted by volume (or signal strength)

Ask: "Ready to cluster and prioritize?"

Phase 4: Analysis & Clustering

Step 4A: Classify Intent

Tag every keyword with search intent:

IntentSignalExample
Informationalhow, what, why, guide, tutorial"synthetic data"
Commercialbest, top, review, platform, tool"data labeling"
Researchdataset, benchmark, model"VLA model"
Transactionalbuy, pricing, discount, free trial"asana pricing"
Step 4B: Assign Priority by Difficulty

Use KD (Keyword Difficulty) when available from paid tool data. Otherwise estimate from SERP competition.

PriorityKD RangeMeaning
Easy Win0-15Low competition — target immediately
Target16-50Winnable with good content
ContentAny KD, but broad/tangentialWrite about it for authority, don't expect to rank
Hard50+Only pursue with strong domain authority
Step 4C: Cluster by Topic

Group keywords into topic clusters. A good cluster has:

  • 1 pillar keyword (broadest term, highest volume)
  • 3-8 supporting keywords (related terms in the same topic area)

Name each cluster with a descriptive label. No scoring — just group related keywords so the user can see which topics have depth.

Step 4D: Identify Top Easy Wins

Extract the best opportunities — keywords with the highest volume-to-difficulty ratio and strong relevance. These are the "do first" list.

Present the clustered, scored list to the user. Ask: "Ready for the final deliverable?"

Phase 5: Deliverable — keywords.csv (STRICT FORMAT)

Your final response must be raw CSV content and nothing else. The harness captures your final output verbatim, saves it as keywords.csv, and validates it against keywords.csv.schema.md. Any deviation fails the artifact.

Absolute rules
  1. No prose before or after the CSV. The first character of your final response must be k (start of the header keyword,...). The last character must be the final character of the last data row.
  2. No code fences. Do not wrap the CSV in ``` or ```csv. Just emit the CSV content.
  3. Exact header, exact order. First row must be:
    keyword,volume,kd,intent,priority,cluster,is_pillar,ai_overview_present,source,notes
  4. Exactly 10 fields per row. Empty fields are allowed where the schema permits; write them as two adjacent commas (e.g. ,,).
  5. Quote fields containing commas, newlines, or double-quotes. Escape embedded " as "".
  6. Minimum 10 data rows. Fewer rows fails validation.
Column contract
#ColumnTypeRequiredAllowed values
1keywordstringyes1–3 words, unique (case is normalized by the harness — write naturally, e.g. GEO tool)
2volumeinteger | emptyno0+; empty if unknown
3kdinteger | emptyno0–100; empty if unknown
4intentenumyesinformational | commercial | research | transactional
5priorityenumyeseasy_win | target | content | hard
6clusterstringyesnon-empty
7is_pillarbooleanyestrue | false
8ai_overview_presentboolean | emptynotrue | false | empty
9sourcestringyesone of ahrefs, semrush, serpapi, autocomplete, paa, reddit, competitor, manual
10notesstringnofree text
Semantic rules
  • Keywords with volume=0 from paid-tool data MUST be removed, not emitted
  • Every cluster must have at least one row with is_pillar=true
  • No duplicate keyword values
  • Apply your intent/priority classification from Phase 4 consistently
Example (what your entire final response must look like)
keyword,volume,kd,intent,priority,cluster,is_pillar,ai_overview_present,source,notes
synthetic data,2400,42,research,target,synthetic_data,true,true,ahrefs,high GEO signal
data labeling,1900,38,commercial,target,synthetic_data,false,true,ahrefs,
vla model,320,12,research,easy_win,robotics_models,true,,serpapi,uncontested niche
robot training,880,35,commercial,target,robotics_models,false,false,ahrefs,
teleoperation,210,28,research,easy_win,robotics_models,false,,paa,strong PAA coverage

(Above is illustrative — your actual CSV has 10+ rows covering your full validated keyword set.)

Strategic notes, blog topics, killed keywords

These do NOT go in the final CSV. If you want to surface them, fold signal into the notes column per row (e.g. notes="polluted by enterprise infra — always use modifiers"). Everything else is dropped for this artifact.

Before emitting

Mentally run through the checklist:

  • Final response starts with keyword,volume,kd,intent,priority,cluster,is_pillar,ai_overview_present,source,notes\n
  • No code fences anywhere
  • No prose before or after
  • ≥ 10 data rows
  • Every row has exactly 10 comma-separated fields
  • Every enum value is from the allowed set (exact spelling)
  • No duplicate keywords
  • Every cluster has one pillar

Then emit the CSV. Nothing else.

Rules

  1. Target keywords must be 1-3 words — this is non-negotiable. Longer phrases go in Blog Topics or GEO Queries. Keywords like "teleoperation data collection for robots" are blog titles, not target keywords.
  2. No fabricated data — do not invent search volumes. Use paid tool data when available, qualitative signals when not. Never estimate or guess a number.
  3. Kill dead keywords early — if a keyword has no autocomplete presence, no PAA, no dedicated SERP results, and no volume in paid tools, remove it. A list of 50 real keywords beats 100 with half dead.
  4. This skill is SEO-focused — SEO keywords (1-3 words) optimize pages for Google ranking. For GEO prompt targeting (full questions for AI citation), use the geo-content-research skill separately.
  5. Real language over marketer language — prioritize the words actual users type, not industry jargon
  6. Interactive — do not skip phases. Get user confirmation before moving to the next phase
  7. Output is actionable — the deliverable should be directly usable for content planning without further analysis
  8. Integrate paid tools when available — Ahrefs/Semrush data is always better than guessing. Offer to integrate it. But the skill works without it too.

© onvoyage-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (scripts) in research-keywords of onvoyage-ai/gtm-engineer-skills.

  • SKILL.md
  • README.md
  • keywords.csv.schema.md
  • scripts/keyword-explorer.mjs
  • scripts/serp-analyzer.mjs

Open the folder on GitHubat commit 3777930

Compare with similar skills

Research Keywords next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Research Keywords compared with similar skills
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Research Keywords this skillonvoyage-ai/gtm-engineer-skills1.3k—~4kAutomated safety check: PassMIT
SEO AgiLeoYeAI/openclaw-master-skills2.2k—~6.4kAutomated safety check: NotesMIT
Geo Fundamentalswasp-lang/wasp19k9 repos~861Automated safety check: PassMIT
Marketing OsYuzzyuk/marketing-os540—~2.5kAutomated safety check: PassMIT
Geoliangdabiao/GEO-Content-Optimizer-Skill2051 repos~2.3kAutomated safety check: NotesMIT
Geo Optimizerliangdabiao/GEO-Content-Optimizer-Skill205—~1.1kAutomated safety check: PassNone

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Works with

Categories

Questions about Research Keywords

What does Research Keywords do?

Finds high-value SEO and GEO keywords using web search, AI analysis, and optionally paid tools like Ahrefs or Semrush. Research Keywords is an agent skill from onvoyage-ai/gtm-engineer-skills. Finds high-value SEO and GEO keywords using web search, AI analysis, and optionally paid tools like Ahrefs or Semrush.

When should I use Research Keywords?

Research Keywords fits situations like: tasks that involve AI search optimization; tasks that involve CSV and tabular files; tasks that involve Web search.

How do I install Research Keywords in Claude Code?

Run `npx skills add onvoyage-ai/gtm-engineer-skills --skill research-keywords -a claude-code`. Or copy the skill folder (research-keywords in onvoyage-ai/gtm-engineer-skills) into .claude/skills/research-keywords in your project. Claude Code loads it when a task matches its description.

How do I install Research Keywords in Codex?

Run `npx skills add onvoyage-ai/gtm-engineer-skills --skill research-keywords -a codex`. Or copy the skill folder (research-keywords in onvoyage-ai/gtm-engineer-skills) into .agents/skills/research-keywords in your project. Codex loads it when a task matches its description.

Can I use Research Keywords in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add onvoyage-ai/gtm-engineer-skills --skill research-keywords -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/research-keywords, .gemini/skills/research-keywords, .github/skills/research-keywords and .opencode/skills/research-keywords in your project.

What does Research Keywords need to run?

Going by SKILL.md and its folder, Research Keywords needs JavaScript for the scripts in its folder. Our summary lists: Node.js.

Does Research Keywords access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Research Keywords safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Research Keywords use?

Research Keywords is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Research Keywords use?

About 4k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Research Keywords?

Skills that share tags, products or a category with Research Keywords: SEO Agi (LeoYeAI/openclaw-master-skills, 2.2k stars), Geo Fundamentals (wasp-lang/wasp, 19k stars), Marketing Os (Yuzzyuk/marketing-os, 540 stars) and Geo (liangdabiao/GEO-Content-Optimizer-Skill, 205 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Research Keywords?

onvoyage-ai (a GitHub organization) maintains it in onvoyage-ai/gtm-engineer-skills, which has 1,320 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on June 7, 2026.

Source: onvoyage-ai/gtm-engineer-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.